






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a:     Any   					=					16
__a:     List[Any]   					=					32




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = 1_6 )				->						Dict:
       _UpperCAmelCase							=    AutoTokenizer.from_pretrained("""bert-base-cased""" )
       _UpperCAmelCase							=    load_dataset("""glue"""   ,      """mrpc""" )

       def tokenize_function(__snake_case ):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase							=    tokenizer(examples["""sentence1"""]   ,      examples["""sentence2"""]   ,      truncation=__snake_case   ,      max_length=__snake_case )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              _UpperCAmelCase							=    datasets.map(
                  __snake_case   ,      batched=__snake_case   ,      remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,      )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _UpperCAmelCase							=    tokenized_datasets.rename_column("""label"""   ,      """labels""" )

       def collate_fn(__snake_case ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _UpperCAmelCase							=    1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _UpperCAmelCase							=    1_6
              elif accelerator.mixed_precision != "no":
                     _UpperCAmelCase							=    8
              else:
                     _UpperCAmelCase							=    None

              return tokenizer.pad(
                  __snake_case   ,      padding="""longest"""   ,      max_length=__snake_case   ,      pad_to_multiple_of=__snake_case   ,      return_tensors="""pt"""   ,      )

       # Instantiate dataloaders.
       _UpperCAmelCase							=    DataLoader(
           tokenized_datasets["""train"""]   ,      shuffle=__snake_case   ,      collate_fn=__snake_case   ,      batch_size=__snake_case   ,      drop_last=__snake_case )
       _UpperCAmelCase							=    DataLoader(
           tokenized_datasets["""validation"""]   ,      shuffle=__snake_case   ,      collate_fn=__snake_case   ,      batch_size=__snake_case   ,      drop_last=(accelerator.mixed_precision == """fp8""")   ,      )

       return train_dataloader, eval_dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[Any]:
       # Initialize accelerator
       _UpperCAmelCase							=    Accelerator(cpu=args.cpu   ,      mixed_precision=args.mixed_precision )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCAmelCase							=    config["""lr"""]
       _UpperCAmelCase							=    int(config["""num_epochs"""] )
       _UpperCAmelCase							=    int(config["""seed"""] )
       _UpperCAmelCase							=    int(config["""batch_size"""] )

       _UpperCAmelCase							=    evaluate.load("""glue"""   ,      """mrpc""" )

       # If the batch size is too big we use gradient accumulation
       _UpperCAmelCase							=    1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _UpperCAmelCase							=    batch_size // MAX_GPU_BATCH_SIZE
              _UpperCAmelCase							=    MAX_GPU_BATCH_SIZE

       set_seed(__snake_case )
       _UpperCAmelCase	,			_UpperCAmelCase							=    get_dataloaders(__snake_case   ,      __snake_case )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase							=    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,      return_dict=__snake_case )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       _UpperCAmelCase							=    model.to(accelerator.device )
       # Instantiate optimizer
       _UpperCAmelCase							=    AdamW(params=model.parameters()   ,      lr=__snake_case )

       # Instantiate scheduler
       _UpperCAmelCase							=    get_linear_schedule_with_warmup(
           optimizer=__snake_case   ,      num_warmup_steps=1_0_0   ,      num_training_steps=(len(__snake_case ) * num_epochs) // gradient_accumulation_steps   ,      )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.

       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(
           __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

       # Now we train the model
       for epoch in range(__snake_case ):
              model.train()
              for step, batch in enumerate(__snake_case ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     _UpperCAmelCase							=    model(**__snake_case )
                     _UpperCAmelCase							=    outputs.loss
                     _UpperCAmelCase							=    loss / gradient_accumulation_steps
                     accelerator.backward(__snake_case )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(__snake_case ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device )
                     with torch.no_grad():
                            _UpperCAmelCase							=    model(**__snake_case )
                     _UpperCAmelCase							=    outputs.logits.argmax(dim=-1 )
                     _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
                     metric.add_batch(
                         predictions=__snake_case   ,      references=__snake_case   ,      )

              _UpperCAmelCase							=    metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							)				->						Optional[int]:
       _UpperCAmelCase							=    argparse.ArgumentParser(description="""Simple example of training script.""" )
       parser.add_argument(
           """--mixed_precision"""   ,      type=__snake_case   ,      default=__snake_case   ,      choices=["""no""", """fp16""", """bf16""", """fp8"""]   ,      help="""Whether to use mixed precision. Choose"""
           """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
           """and an Nvidia Ampere GPU."""   ,      )
       parser.add_argument("""--cpu"""   ,      action="""store_true"""   ,      help="""If passed, will train on the CPU.""" )
       _UpperCAmelCase							=    parser.parse_args()
       _UpperCAmelCase							=    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
       training_function(__snake_case   ,      __snake_case )


if __name__ == "__main__":
  main()

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   List[str]						=			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ReformerTokenizer
	_A								=	ReformerTokenizerFast
	_A								=	True
	_A								=	False
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								super().setUp()

								__UpperCamelCase      :  List[str]       =   ReformerTokenizer(a	,  keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "<s>"
								__UpperCamelCase      :  Dict       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a		)	,  a		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a		)	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  int       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<unk>"		)
								self.assertEqual(vocab_keys[1]	,  "<s>"		)
								self.assertEqual(vocab_keys[-1]	,  "j"		)
								self.assertEqual(len(a		)	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase      :  Union[str, Any]       =   self.get_tokenizer()
								__UpperCamelCase      :  List[str]       =   self.get_rust_tokenizer()

								__UpperCamelCase      :  Union[str, Any]       =   "I was born in 92000, and this is falsé."

								__UpperCamelCase      :  List[str]       =   tokenizer.tokenize(a		)
								__UpperCamelCase      :  int       =   rust_tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  List[str]       =   tokenizer.encode(a	,  add_special_tokens=a		)
								__UpperCamelCase      :  Tuple       =   rust_tokenizer.encode(a	,  add_special_tokens=a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.get_rust_tokenizer()
								__UpperCamelCase      :  int       =   tokenizer.encode(a		)
								__UpperCamelCase      :  List[Any]       =   rust_tokenizer.encode(a		)
								self.assertListEqual(a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:int=1_5		)						->					Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase      :  List[Any]       =   self.rust_tokenizer_class.from_pretrained(a	,  **a		)

																						# Simple input
																						__UpperCamelCase      :  Union[str, Any]       =   "This is a simple input"
																						__UpperCamelCase      :  Dict       =   ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase      :  Tuple       =   ("This is a simple input", "This is a pair")
																						__UpperCamelCase      :  Optional[Any]       =   [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					str:
								pass







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  List[Any]       =   ReformerTokenizer(a	,  keep_accents=a		)

								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a	,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

								__UpperCamelCase      :  str       =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]	,  )
								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]	,  )







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "Hello World!"
								__UpperCamelCase      :  Union[str, Any]       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Dict       =   (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								__UpperCamelCase      :  List[Any]       =   [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase      :  Tuple       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase      :  Union[str, Any]       =   " ".join(a		)
								__UpperCamelCase      :  Tuple       =   self.big_tokenizer.encode_plus(a	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]	,  return_tensors="pt"		)

								__UpperCamelCase      :  Optional[Any]       =   ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase      :  Tuple       =   encoded_sequence["input_ids"].shape
								__UpperCamelCase      :  int       =   ReformerModel(a		)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
															model(**a		)
															model(**a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								# fmt: off
								__UpperCamelCase      :  Any       =   {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								__UpperCamelCase      :  List[str]       =   [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=a	,  model_name="google/reformer-crime-and-punishment"	,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"	,  padding=a	,  sequences=a	,  )